Scaling Transformer Models with µ-Transfer: A Comprehensive Empirical Study

核心概念

Empirical investigation of the reliability and limitations of the µ-Transfer technique for scaling hyperparameters, particularly learning rates, across transformer models of varying sizes.

摘要

This paper presents a large-scale empirical study on the reliability and limitations of the µ-Transfer technique for scaling hyperparameters, particularly learning rates, across transformer models of varying sizes.

The key highlights and insights are:

1. The authors establish baseline results showing that µ-Transfer works reliably for scaling learning rates across transformer models ranging from 2M to 10B parameters, when using standard architectural choices.

2. The authors investigate the impact of various architectural modifications, such as using projection biases, RMSNorm gains, different attention scales, and multiplicative nonlinearities. They find that µ-Transfer is compatible with most of these changes, but can break down when using trainable scale parameters in the network.

3. The authors conduct the largest-scale µ-Transfer experiment to date, demonstrating that the optimal learning rate found for a 2M parameter model accurately predicts the optimum for a 10B parameter model.

4. The authors also explore the compatibility of µ-Transfer with other techniques like decoupled weight decay, large and small batch sizes, and the Lion optimizer. They find that µ-Transfer generally works well, with some exceptions.

Overall, the results provide a comprehensive empirical understanding of the strengths and limitations of the µ-Transfer technique for scaling transformer models, and offer guidance for practitioners on its practical application.

统计

The paper reports the following key metrics and figures:
"The largest model size here is 1.2B parameters and all models train for 33B tokens."
"Our largest-scale µ-transfer results" show models ranging from 2M to 10B parameters.

引用

None.

从中提取的关键见解

A Large-Scale Exploration of $μ$-Transfer

by Lucas Lingle 在 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05728.pdf

更深入的查询

How might the µ-Transfer technique be extended or modified to handle architectural choices that disrupt the optimal hyperparameter transfer, such as the use of trainable scale parameters

To handle architectural choices that disrupt the optimal hyperparameter transfer in the context of trainable scale parameters, the µ-Transfer technique could be extended or modified in several ways. One approach could involve incorporating adaptive scaling factors for the trainable scale parameters based on the model size. By dynamically adjusting the scaling factors during µ-Transfer, the technique could potentially mitigate the disruption caused by the trainable scale parameters. Additionally, introducing regularization techniques specific to the scale parameters, such as weight constraints or penalties, could help stabilize the transfer process. Another strategy could involve conducting targeted experiments to understand the impact of different scale parameter configurations on hyperparameter transfer and devising specific rules or adjustments within the µ-Parameterization framework to address the challenges posed by trainable scale parameters.

What other hyperparameters beyond learning rates, such as weight decay or batch size, could be investigated for transfer using the µ-Parameterization framework

Beyond learning rates, the µ-Parameterization framework could be used to investigate the transfer of other hyperparameters such as weight decay and batch size. For weight decay, µ-Transfer could be applied to determine the optimal weight decay values for larger models based on the settings that work best for smaller proxy models. This could involve scaling the weight decay hyperparameter according to the model size, similar to the approach used for learning rates in the framework. Additionally, exploring the transferability of batch size settings through µ-Transfer could provide insights into how batch size impacts model performance and training dynamics across different model scales. By systematically studying the effects of varying weight decay and batch size on model training and performance, µ-Parameterization could offer guidelines for setting these hyperparameters in large-scale language models.

Given the success of µ-Transfer in predicting the optimal learning rate for a 10B parameter model from a 2M parameter proxy, are there other ways this technique could be leveraged to reduce the computational burden of hyperparameter tuning for very large language models

The success of µ-Transfer in predicting the optimal learning rate for a 10B parameter model from a 2M parameter proxy opens up possibilities for leveraging this technique to reduce the computational burden of hyperparameter tuning for very large language models. One potential application could involve using the µ-Parameterization framework to establish transfer rules for a broader range of hyperparameters beyond just learning rates. By extending the µ-Transfer approach to encompass a comprehensive set of hyperparameters, researchers and practitioners could streamline the hyperparameter tuning process for large models, saving computational resources and time. Furthermore, µ-Transfer could be integrated into automated hyperparameter optimization pipelines to facilitate efficient and effective tuning of hyperparameters for extremely large language models, enabling researchers to focus on model development and experimentation rather than manual hyperparameter tuning.